The Department of Computer Science is excited to welcome five new tenure-track faculty members.
Sungjin Ahn received his Ph.D. at the University of California, Irvine under the supervision of Prof. Max Welling. He was then a postdoc under Prof. Yoshua Bengio at the University of Montreal and MILA. His research area is machine learning, particularly in the areas of deep learning, Bayesian learning, reinforcement learning, and neuro & cognitive science-inspired learning. His current research focus is probabilisitic generative models, meta learning, and representation learning to advance agent/robot learning. His goal is to make a general AI agent that can learn like humans in complex environments.
Aaron Bernstein is interested in the design and analysis of algorithms, with a focus on algorithms that are able to handle the challenges posed by massive data sets. Prior to joining Rutgers, he did a postdoc with Martin Skutella at the Berlin University of Technology, funded by the Einstein Fellowship. He received his PhD from Columbia University, where he was advised by Clifford Stein, and supported by the NSF graduate fellowship and a fellowship from the Simons Institute. His thesis was on algorithms for maintaining information in graphs that evolve over time. He has received multiple best paper awards from top conferences in theoretical computer science, such as Symposium on Theory of Computing and Symposium on Discrete Algorithms.
Dong Deng is interested in database and data science research, with a special focus on building systems and developing algorithms for data curation, data integration, and data cleaning. Before joining Rutgers, he conducted a postdoc research with Mike Stonebraker and Sam Madden in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT. Dong obtained his PhD degree from Tsinghua University with the highest doctoral dissertation award. He also received the Siebel Scholarship, Google PhD Fellowship, and Microsoft PhD Fellowship during his PhD study. He has been regularly publishing in top database venues like SIGMOD, PVLDB, and ICDE.
Sudarsun Kannan is interested in operating systems, with a focus towards heterogeneous resource (memory, storage, and compute) management challenges and understanding their impact on large-scale applications. Before joining Rutgers, he was a postdoc at the University of Wisconsin-Madison's CS department advised by Prof. Andrea Arpaci-Dusseau and Prof. Remzi Arpaci-Dusseau where he worked on designing device-level file systems. Prior to his postdoc, he graduated from the College of Computing, Georgia Tech, where he was advised by the late Prof. Karsten Schwan and Prof. Ada Gavrilovska. His thesis explored methods to extend virtual memory support for heterogeneous memory technologies.
Srinivas Narayana's research aims to design computer networks that are highly programmable and easy to manage. Programming abstractions enable network operators to diagnose poor application performance, developers to build high-performance applications, and hardware architects to design mechanisms that allocate resources efficiently. Srinivas's research combines insights from network protocol design, programming languages, compilers, operating systems, computer architecture, and databases. Srinivas received his Ph.D. in Computer Science from Princeton University and joined the Department of Computer Science at Rutgers after a postdoctoral position at Massachusetts Institute of Technology.
